#### Command list

• Input handling
• Solution control and techniques
• Output
• Mesh commands
• Nodes and connectivity
• Material properties
• Initial conditions
• Boundary conditions
• Contact and tied interfaces
• Rigid bodies
• Connectors
• Parameters and functions
• Geometries
• Sets
• Coordinate system
• Particle
• SPH
Functions & parameters

### Functions & parameters

The following built in functions and parameters are supported by the commands FUNCTION and PARAMETER. They can also be used when defining expressions replacing numerical values in the input deck.

Function / parameter Description
pi 3.141592653589793...
abs(x) Absolute value
d(i,j) Kronecker delta (d(i,j)=1 if i=j; d(i,j)=0 if i $\neq$ j)
erf(x) Gauss error function
fcn(ID, x) Returns a CURVE or FUNCTION value. $x$ is the abscissa.
H(x) Step function (H(x<0) = 0; H(x $\geq$ 0) = 1)
ln(x) or log(x) Natural logarithm
log10(x) Logarithm to base 10
sign(x) Sign function (sign(x<0) = -1; sign(x $\geq$ 0) = 1)
min(x${}_1$, x${}_2$, ... x${}_n$) Min function
max(x${}_1$, x${}_2$, ... x${}_n$) Max function
sin(x) Trigonometric sine function (degrees)
cos(x) Trigonometric cosine function (degrees)
tan(x) Trigonometric tangent function (degrees)
exp(x) Exponential function (exp(x) = $e^x$)
^ Exponent (x^y = $x^y$)
norminv($\mu, \sigma, P, cap$) Normal distribution quantile (inverse cumulative distribution function). The function returns a value in a normal distribution. Required input is the median value $\mu$, standard deviation $\sigma$ and a cumulative point in the distribution $0 \lt P \lt 1$ (typically a random number). $cap$ is optional and limits the deviation from $\mu$ (max number of standard deviations)
lognorminv($\mu, \sigma, P, cap$) Log-normal distribution quantile (inverse cumulative distribution function). The function returns a value in a log-normal distribution. $\mathrm{exp}(\mu)$ is the median value in the distribution, $\sigma$ is the standard deviation of the variable's natural logarithm and $0 \lt P \lt 1$ is a cumulative point in the distribution (typically a random number). $cap$ is optional and limits the deviation from $\mu$ (max number of standard deviations)